Feed forward neural network using numpy for IRIS dataset
You can not use the ready code directly without any manipulation. Because every piece of code is written for specific datasets. In the article that you mentioned, the writer created a small dataset and then he creates an ANN architecture for it. If you want to build an ANN based on Iris dataset, you should think and create an architecture on paper maybe before coding. You should understand the Iris dataset first, I mean you should understand what is going to be your input shapes, and also the shape of weights are related to inputs' shape. After understanding these you should decide the number of layer of your Neural Network. And that tutorial that you followed is explaining all of these clearly. Do not skip and understand everything steps by step.

If you want a build ANN architecture by yourself, you must understand every mathematics behind that. (forward and backward propagation, loss and cost, gradient descent) If you do not want to deal with all of these maths, you can use a library such as Keras, then you can create an ANN easily.

See this as a source, https://ml-cheatsheet.readthedocs.io/en/latest/
And you see my Kaggle kernel, I create this network with details and only using numpy. Also I used Keras on another section. You can check; https://www.kaggle.com/erdemuysal/gender-recognition-with-lr-and-ann